OpenRaiser/Intern-Atlas huggingface dataset:
CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge
Venue: arXiv cs.IT 2026
Year: 2026

The deployment of large-scale neural networks within the Open Radio Access Network (O-RAN) architecture is pivotal for enabling native edge intelligence. However, this paradigm faces two critical bottlenecks: the prohibitive memory footprint required for local training on resource-constrained gNBs, and the saturation of bandwidth-limited backhaul links during the global aggregation of high-dimensional model updates. To address these challenges, we propose CoCo-Fed, a novel Compression and Combination-based Federated learning framework that unifies local memory efficiency and global communication reduction. Locally, CoCo-Fed breaks the memory wall by performing a double-dimension down-projection of gradients, adapting the optimizer to operate on low-rank structures without introducing additional inference parameters/latency. Globally, we introduce a transmission protocol based on orthogonal subspace superposition, where layer-wise updates are projected and superimposed into a single consolidated matrix per gNB, drastically reducing the backhaul traffic. Beyond empirical designs, we establish a rigorous theoretical foundation, proving the convergence of CoCo-Fed even under unsupervised learning conditions suitable for wireless sensing tasks. Extensive simulations on an angle-of-arrival estimation task demonstrate that CoCo-Fed significantly outperforms state-of-the-art baselines in both memory and communication efficiency while maintaining robust convergence under non-IID settings.
Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
Venue: arXiv cs.LG 2026
Year: 2026

To enhance the reproducibility and reliability of deep learning models, we address a critical gap in current training methodologies: the lack of mechanisms that ensure consistent and robust performance across runs. Our empirical analysis reveals that even under controlled initialization and training conditions, the accuracy of the model can exhibit significant variability. To address this issue, we propose a Custom Loss Function (CLF) that reduces the sensitivity of training outcomes to stochastic factors such as weight initialization and data shuffling. By fine-tuning its parameters, CLF explicitly balances predictive accuracy with training stability, leading to more consistent and reliable model performance. Extensive experiments across diverse architectures for both image classification and time series forecasting demonstrate that our approach significantly improves training robustness without sacrificing predictive performance. These results establish CLF as an effective and efficient strategy for developing more stable, reliable and trustworthy neural networks.
The AI Invisibility Effect: Understanding Human-AI Interaction When Users Don't Recognize Artificial Intelligence
Venue: arXiv cs.HC 2026
Year: 2026

The fast integration of artificial intelligence into mobile applications has completely changed the digital landscape; however, the impact of this change on user perception of AI features remains poorly understood. This large-scale analysis examined 1,484,633 mobile application reviews across 422 applications (200 AI-featuring, 222 control) from iOS App Store and Google Play Store. By employing sentiment classification, topic modeling, and concern-benefit categorization, we identified a major disconnect: only 11.9% of reviews mentioned AI, even though 47.4% of applications featured AI capabilities. AI-featuring applications received significantly lower ratings than traditional applications (d = 0.40); however, hierarchical regression revealed a hidden pattern - the negative relationship reversed after controlling for AI mentions and review characteristics (b = 0.405, p < .001). Privacy dominated user concerns (34.8% of concern-expressing reviews), while efficiency represented the primary benefit (42.3%). Effects varied greatly by category, from positive for Assistant applications (d = 0.55) to negative for Entertainment (d = -0.23). These findings suggest that AI features often operate below user awareness thresholds, and it is the explicit recognition of AI, rather than its mere presence, that drives negative evaluations. This challenges basic assumptions about technology acceptance in AI systems.
Noise-Robust Tiny Object Localization with Flows
Venue: arXiv cs.CV 2026
Year: 2026

Despite significant advances in generic object detection, a persistent performance gap remains for tiny objects compared to normal-scale objects. We demonstrate that tiny objects are highly sensitive to annotation noise, where optimizing strict localization objectives risks noise overfitting. To address this, we propose Tiny Object Localization with Flows (TOLF), a noise-robust localization framework leveraging normalizing flows for flexible error modeling and uncertainty-guided optimization. Our method captures complex, non-Gaussian prediction distributions through flow-based error modeling, enabling robust learning under noisy supervision. An uncertainty-aware gradient modulation mechanism further suppresses learning from high-uncertainty, noise-prone samples, mitigating overfitting while stabilizing training. Extensive experiments across three datasets validate our approach's effectiveness. Especially, TOLF boosts the DINO baseline by 1.2% AP on the AI-TOD dataset.
HyperPriv-EPN: Hypergraph Learning with Privileged Knowledge for Ependymoma Prognosis
Venue: arXiv cs.CV 2026
Year: 2026

Preoperative prognosis of Ependymoma is critical for treatment planning but challenging due to the lack of semantic insights in MRI compared to post-operative surgical reports. Existing multimodal methods fail to leverage this privileged text data when it is unavailable during inference. To bridge this gap, we propose HyperPriv-EPN, a hypergraph-based Learning Using Privileged Information (LUPI) framework. We introduce a Severed Graph Strategy, utilizing a shared encoder to process both a Teacher graph (enriched with privileged post-surgery information) and a Student graph (restricted to pre-operation data). Through dual-stream distillation, the Student learns to hallucinate semantic community structures from visual features alone. Validated on a multi-center cohort of 311 patients, HyperPriv-EPN achieves state-of-the-art diagnostic accuracy and survival stratification. This effectively transfers expert knowledge to the preoperative setting, unlocking the value of historical post-operative data to guide the diagnosis of new patients without requiring text at inference.
KELP: Robust Online Log Parsing Through Evolutionary Grouping Trees
Venue: arXiv cs.DB 2026
Year: 2026

Real-time log analysis is the cornerstone of observability for modern infrastructure. However, existing online parsers are architecturally unsuited for the dynamism of production environments. Built on fundamentally static template models, they are dangerously brittle: minor schema drifts silently break parsing pipelines, leading to lost alerts and operational toil. We propose \textbf{KELP} (\textbf{K}elp \textbf{E}volutionary \textbf{L}og \textbf{P}arser), a high-throughput parser built on a novel data structure: the Evolutionary Grouping Tree. Unlike heuristic approaches that rely on fixed rules, KELP treats template discovery as a continuous online clustering process. As logs arrive, the tree structure evolves, nodes split, merge, and re-evaluate roots based on changing frequency distributions. Validating this adaptability requires a dataset that models realistic production complexity, yet we identify that standard benchmarks rely on static, regex-based ground truths that fail to reflect this. To enable rigorous evaluation, we introduce a new benchmark designed to reflect the structural ambiguity of modern production systems. Our evaluation demonstrates that KELP maintains high accuracy on this rigorous dataset where traditional heuristic methods fail, without compromising throughput. Our code and dataset can be found at codeberg.org/stonebucklabs/kelp
Physio-DPO: Aligning Large Language Models with the Protein Energy Landscape to Eliminate Structural Hallucinations
Venue: arXiv cs.CL 2026
Year: 2026

Large Protein Language Models have shown strong potential for generative protein design, yet they frequently produce structural hallucinations, generating sequences with high linguistic likelihood that fold into thermodynamically unstable conformations. Existing alignment approaches such as Direct Preference Optimization are limited in this setting, as they model preferences as binary labels and ignore the continuous structure of the physical energy landscape. We propose Physio-DPO, a physics informed alignment framework that grounds protein language models in thermodynamic stability. Physio-DPO introduces a magnitude aware objective that scales optimization updates according to the energy gap between native structures and physics perturbed hard negatives. Experiments show that Physio-DPO consistently outperforms strong baselines including SFT, PPO, and standard DPO, reducing self consistency RMSD to 1.28 Å and increasing foldability to 92.8%. Qualitative analysis further demonstrates that Physio-DPO effectively mitigates structural hallucinations by recovering biophysical interactions such as hydrophobic core packing and hydrogen bond networks.
CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Despite the rapid success of Large Vision-Language Models (LVLMs), a persistent challenge is their tendency to generate hallucinated content, undermining reliability in real-world use. Existing training-free methods address hallucinations but face two limitations: (i) they rely on narrow assumptions about hallucination sources, and (ii) their effectiveness declines toward the end of generation, where hallucinations are most likely to occur. A common strategy is to build hallucinated models by completely or partially removing visual tokens and contrasting them with the original model. Yet, this alone proves insufficient, since visual information still propagates into generated text. Building on this insight, we propose a novel hallucinated model that captures hallucination effects by selectively removing key text tokens. We further introduce Generalized Contrastive Decoding, which integrates multiple hallucinated models to represent diverse hallucination sources. Together, these ideas form CRoPS, a training-free hallucination mitigation framework that improves CHAIR scores by 20% and achieves consistent gains across six benchmarks and three LVLM families, outperforming state-of-the-art training-free methods.
Scheduling for TWDM-EPON-Based Fronthaul Without a Dedicated Registration Wavelength
Venue: arXiv cs.NI 2026
Year: 2026

The adoption of Centralized Radio Access Network (C-RAN) architectures requires fronthaul systems capable of carrying large volumes of radio data while meeting stringent delay and jitter requirements. Ethernet Passive Optical Networks (EPONs) have emerged as a promising fronthaul solution due to their cost efficiency and compatibility with existing infrastructure. However, the traditional registration process for EPON systems halts the ongoing data transmissions during the registration period, thereby violating the enhanced Common Public Radio Interface (eCPRI) delay and jitter requirements. This limitation has been acknowledged by the ITU-T, which recommends the use of a dedicated wavelength channel for registration, leading to inefficient bandwidth utilization. In this paper, we propose a novel scheduling framework for a Time and Wavelength Division Multiplexed (TWDM) EPON-based fronthaul that enables periodic registration without wasting an additional wavelength channel. Performance evaluation demonstrates that the proposed method achieves up to a 71\% increase in the number of Radio Units (RUs) supported for a given number of wavelength channels, compared to a baseline scheme employing a dedicated registration wavelength.
ARISE: Adaptive Reinforcement Integrated with Swarm Exploration
Venue: arXiv cs.LG 2026
Year: 2026

Effective exploration remains a key challenge in RL, especially with non-stationary rewards or high-dimensional policies. We introduce ARISE, a lightweight framework that enhances reinforcement learning by augmenting standard policy-gradient methods with a compact swarm-based exploration layer. ARISE blends policy actions with particle-driven proposals, where each particle represents a candidate policy trajectory sampled in the action space, and modulates exploration adaptively using reward-variance cues. While easy benchmarks exhibit only slight improvements (e.g., +0.7% on CartPole-v1), ARISE yields substantial gains on more challenging tasks, including +46% on LunarLander-v3 and +22% on Hopper-v4, while preserving stability on Walker2d and Ant. Under non-stationary reward shifts, ARISE provides marked robustness advantages, outperforming PPO by +75 points on CartPole and improving LunarLander accordingly. Ablation studies confirm that both the swarm component and the adaptive mechanism contribute to the performance. Overall, ARISE offers a simple, architecture-agnostic route to more exploratory and resilient RL agents without altering core algorithmic structures.
Bayesian Inverse Games with High-Dimensional Multi-Modal Observations
Venue: arXiv cs.LG 2026
Year: 2026

Many multi-agent interaction scenarios can be naturally modeled as noncooperative games, where each agent's decisions depend on others' future actions. However, deploying game-theoretic planners for autonomous decision-making requires a specification of all agents' objectives. To circumvent this practical difficulty, recent work develops maximum likelihood techniques for solving inverse games that can identify unknown agent objectives from interaction data. Unfortunately, these methods only infer point estimates and do not quantify estimator uncertainty; correspondingly, downstream planning decisions can overconfidently commit to unsafe actions. We present an approximate Bayesian inference approach for solving the inverse game problem, which can incorporate observation data from multiple modalities and be used to generate samples from the Bayesian posterior over the hidden agent objectives given limited sensor observations in real time. Concretely, the proposed Bayesian inverse game framework trains a structured variational autoencoder with an embedded differentiable Nash game solver on interaction datasets and does not require labels of agents' true objectives. Extensive experiments show that our framework successfully learns prior and posterior distributions, improves inference quality over maximum likelihood estimation-based inverse game approaches, and enables safer downstream decision-making without sacrificing efficiency. When trajectory information is uninformative or unavailable, multimodal inference further reduces uncertainty by exploiting additional observation modalities.
DefVINS: Visual-Inertial Odometry for Deformable Scenes
Venue: arXiv cs.RO 2026
Year: 2026

Deformable scenes violate the rigidity assumptions underpinning classical visual-inertial odometry (VIO), often leading to over-fitting to local non-rigid motion or severe drift when deformation dominates visual parallax. We introduce DefVINS, a visual-inertial odometry framework that explicitly separates a rigid, IMU-anchored state from a non--rigid warp represented by an embedded deformation graph. The system is initialized using a standard VIO procedure that fixes gravity, velocity, and IMU biases, after which non-rigid degrees of freedom are activated progressively as the estimation becomes well conditioned. An observability analysis is included to characterize how inertial measurements constrain the rigid motion and render otherwise unobservable modes identifiable in the presence of deformation. This analysis motivates the use of IMU anchoring and informs a conditioning-based activation strategy that prevents ill-posed updates under poor excitation. Ablation studies demonstrate the benefits of combining inertial constraints with observability-aware deformation activation, resulting in improved robustness under non-rigid environments.
PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy Among University Students
Venue: arXiv cs.CY 2026
Year: 2026

Personal data privacy literacy (PDPL) refers to a collection of digital literacy skills related to an individuals ability to understand, evaluate, and manage the collection, use, and protection of personal data in online and digital environments. This study introduces and validates a new psychometric scale (PDPL Metric) designed to measure data privacy literacy among university students, focusing on six key privacy constructs: perceived risk of data misuse, expectations of informed consent, general privacy concern, privacy management awareness, privacy-utility trade-off acceptance, and perceived importance of data security. A 24-item questionnaire was developed and administered to students at U.S.-based research universities. Principal components analysis confirmed the unidimensionality and internal consistency of each construct, and a second-order analysis supported the integration of all six into a unified PDPL construct. No differences in PDPL were found based on basic demographic variables like academic level and gender, although a difference was found based on domestic/international status. The findings of this study offer a validated framework for assessing personal data privacy literacy within the higher education context and support the integration of the core constructs into higher education programs, organizational policies, and digital literacy initiatives on university campuses.
Multi-Level Feature Fusion for Continual Learning in Visual Quality Inspection
Venue: arXiv cs.CV 2026
Year: 2026

Deep neural networks show great potential for automating various visual quality inspection tasks in manufacturing. However, their applicability is limited in more volatile scenarios, such as remanufacturing, where the inspected products and defect patterns often change. In such settings, deployed models require frequent adaptation to novel conditions, effectively posing a continual learning problem. To enable quick adaptation, the necessary training processes must be computationally efficient while still avoiding effects like catastrophic forgetting. This work presents a multi-level feature fusion (MLFF) approach that aims to improve both aspects simultaneously by utilizing representations from different depths of a pretrained network. We show that our approach is able to match the performance of end-to-end training for different quality inspection problems while using significantly less trainable parameters. Furthermore, it reduces catastrophic forgetting and improves generalization robustness to new product types or defects.
Grading Handwritten Engineering Exams with Multimodal Large Language Models
Venue: arXiv cs.CV 2026
Year: 2026

Handwritten STEM exams capture open-ended reasoning and diagrams, but manual grading is slow and difficult to scale. We present an end-to-end workflow for grading scanned handwritten engineering quizzes with multimodal large language models (LLMs) that preserves the standard exam process (A4 paper, unconstrained student handwriting). The lecturer provides only a handwritten reference solution (100%) and a short set of grading rules; the reference is converted into a text-only summary that conditions grading without exposing the reference scan. Reliability is achieved through a multi-stage design with a format/presence check to prevent grading blank answers, an ensemble of independent graders, supervisor aggregation, and rigid templates with deterministic validation to produce auditable, machine-parseable reports. We evaluate the frozen pipeline in a clean-room protocol on a held-out real course quiz in Slovenian, including hand-drawn circuit schematics. With state-of-the-art backends (GPT-5.2 and Gemini-3 Pro), the full pipeline achieves $\approx$8-point mean absolute difference to lecturer grades with low bias and an estimated manual-review trigger rate of $\approx$17% at $D_{\max}=40$. Ablations show that trivial prompting and removing the reference solution substantially degrade accuracy and introduce systematic over-grading, confirming that structured prompting and reference grounding are essential.
An Agentic Framework for Neuro-Symbolic Programming
Venue: arXiv cs.AI 2026
Year: 2026

Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming and challenging task. Existing frameworks like DomiKnowS help this integration by providing a high-level declarative programming interface, but they still assume the user is proficient with the library's specific syntax. We propose AgenticDomiKnowS (ADS) to eliminate this dependency. ADS translates free-form task descriptions into a complete DomiKnowS program using an agentic workflow that creates and tests each DomiKnowS component separately. The workflow supports optional human-in-the-loop intervention, enabling users familiar with DomiKnowS to refine intermediate outputs. We show how ADS enables experienced DomiKnowS users and non-users to rapidly construct neuro-symbolic programs, reducing development time from hours to 10-15 minutes.
A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football
Venue: arXiv cs.LG 2026
Year: 2026

Evaluating off-ball defensive performance in football is challenging, as traditional metrics do not capture the nuanced coordinated movements that limit opponent action selection and success probabilities. Although widely used possession value models excel at appraising on-ball actions, their application to defense remains limited. Existing counterfactual methods, such as ghosting models, help extend these analyses but often rely on simulating "average" behavior that lacks tactical context. To address this, we introduce a covariate-dependent Hidden Markov Model (CDHMM) tailored to corner kicks, a highly structured aspect of football games. Our label-free model infers time-resolved man-marking and zonal assignments directly from player tracking data. We leverage these assignments to propose a novel framework for defensive credit attribution and a role-conditioned ghosting method for counterfactual analysis of off-ball defensive performance. We show how these contributions provide a interpretable evaluation of defensive contributions against context-aware baselines.
Categorical Reparameterization with Denoising Diffusion models
Venue: arXiv cs.LG 2026
Year: 2026

Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient-based optimization is challenging due to the non-differentiability of categorical sampling. A common workaround is to replace the discrete distribution with a continuous relaxation, yielding a smooth surrogate that admits reparameterized gradient estimates via the reparameterization trick. Building on this idea, we introduce ReDGE, a novel and efficient diffusion-based soft reparameterization method for categorical distributions. Our approach defines a flexible class of gradient estimators that includes the Straight-Through estimator as a special case. Experiments spanning latent variable models and inference-time reward guidance in discrete diffusion models demonstrate that ReDGE consistently matches or outperforms existing gradient-based methods. The code will be made available at https://github.com/samsongourevitch/redge.